Tag: education

I would argue, in total seriousness, that one of the places that Skinnerism thrives today is in computing technologies, particularly in “social” technologies. This, despite the field’s insistence that its development is a result, in part, of the cognitive turn that supposedly displaced behaviorism.

B. J. Fogg and his Persuasive Technology Lab at Stanford is often touted by those in Silicon Valley as one of the “innovators” in this “new” practice of building “hooks” and “nudges” into technology. These folks like to point to what’s been dubbed colloquially “The Facebook Class” – a class Fogg taught in which students like Kevin Systrom and Mike Krieger, the founders of Instagram, and Nir Eyal, the author of Hooked, “studied and developed the techniques to make our apps and gadgets addictive,” as Wired put it in a recent article talking about how some tech executives now suddenly realize that this might be problematic.

(It’s worth teasing out a little – but probably not in this talk, since I’ve rambled on so long already – the difference, if any, between “persuasion” and “operant conditioning” and how they imagine to leave space for freedom and dignity. Rhetorically and practically.)

I’m on the record elsewhere arguing this framing – “technology as addictive” – has its problems. Nevertheless it is fair to say that the kinds of compulsive behavior that we display with our apps and gadgets is being encouraged by design. All that pecking. All that clicking.

These are “technologies of behavior” that we can trace back to Skinner – perhaps not directly, but certainly indirectly due to Skinner’s continual engagement with the popular press. His fame and his notoriety. Behavioral management – and specifically through operant conditioning – remains a staple of child rearing and pet training. It is at the core of one of the most popular ed-tech apps currently on the market, ClassDojo. Behaviorism also underscores the idea that how we behave and data about how we behave when we click can give programmers insight into how to alter their software and into what we’re thinking.

If we look more broadly – and Skinner surely did – these sorts of technologies of behavior don’t simply work to train and condition individuals; many technologies of behavior are part of a broader attempt to reshape society. “For your own good,” the engineers try to reassure us. “For the good of the world.”

In that Baffler article, I make the argument that behavior management apps like ClassDojo’s are the latest manifestation of behaviorism, a psychological theory that has underpinned much of the development of education technology. Behaviorism is, of course, most closely associated with B. F. Skinner, who developed the idea of his “teaching machine” when he visited his daughter’s fourth grade class in 1953. Skinner believed that a machine could provide a superior form of reinforcement to the human teacher, who relied too much on negative reinforcement, punishing students for bad behavior than on positive reinforcement, the kind that better trains the pigeons.

But I think there’s been a resurgence in behaviorism. It’s epicenter isn’t Harvard, where Skinner taught. It’s Stanford. It’s Silicon Valley. And this new behaviorism is fundamental to how many new digital technologies are being built.

There’s a darker side still to this as I argued in the first article in this very, very long series: this kind of behavior management has become embedded in our new information architecture. It’s “fake news,” sure. But it’s also disinformation plus big data plus psychological profiling and behavior modification. The Silicon Valley “nudge” is a corporatenudge. But as these technologies are increasingly part of media, scholarship, and schooling, it’s a civics nudge too.

Those darling little ClassDojo monsters are a lot less cute when you see them as part of a new regime of educational data science, experimentation, and “psycho-informatics.”

One of my favorite anecdotes from Asperger’s thesis is when he asks an autistic boy in his clinic if he believes in God. “I don’t like to say I’m not religious,” the boy replies, “I just don’t have any proof of God.” That anecdote shows an appreciation of autistic non-compliance, which Asperger and his colleagues felt was as much a part of their patients’ autism as the challenges they faced. Asperger even anticipated in the 1970s that autistic adults who “valued their freedom” would object to behaviorist training, and that has turned out to be true.

Plenty of policies and programs limit our ability to do right by children. But perhaps the most restrictive virtual straitjacket that educators face is behaviorism – a psychological theory that would have us focus exclusively on what can be seen and measured, that ignores or dismisses inner experience and reduces wholes to parts. It also suggests that everything people do can be explained as a quest for reinforcement – and, by implication, that we can control others by rewarding them selectively.

Allow me, then, to propose this rule of thumb: The value of any book, article, or presentation intended for teachers (or parents) is inversely related to the number of times the word “behavior” appears in it. The more our attention is fixed on the surface, the more we slight students’ underlying motives, values, and needs.

It’s been decades since academic psychology took seriously the orthodox behaviorism of John B. Watson and B.F. Skinner, which by now has shrunk to a cult-like clan of “behavior analysts.” But, alas, its reductionist influence lives on – in classroom (and schoolwide) management programs like PBIS and Class Dojo, in scripted curricula and the reduction of children’s learning to “data,” in grades and rubrics, in “competency”- and “proficiency”-based approaches to instruction, in standardized assessments, in reading incentives and merit pay for teachers.>

It’s time we outgrew this limited and limiting psychological theory. That means attending less to students’ behaviors and more to the students themselves.

When learning is allowed to be project, problem, and passion driven, then children learn because of their terroir, not disengage in spite of it. When we recognize biodiversity in our schools as healthy, then we increase the likelihood that our ecosystems will thrive.

To be contributors to educating children to live in a world that is increasingly challenging to negotiate, schools must be ​conceptualized as ecological communities, spaces for learning with the potential to embody all of the concepts of the ecosystem – interactivity, biodiversity, connections, adaptability, succession, and balance. These concepts have become a lens through which we consider and understand the schools we observe and what makes learning thrive in some spaces and not others.

The problem is that standardization becomes the antithesis of creativity in schools. There’s no “follow the questions” inquiry or problem‐ and project‐driven assessments in standardized classrooms. Covering the standardized curricula means rejecting the biodiversity of communities that have the potential to generate new ways of thinking based on their own unique environments. Those statistical norms that drive much of standardized practice seem to be built for mythical school communities, model neighborhood schools where we expect students to succeed in the same way. Using “teacher‐proof” assessments and programs makes a lot of sense if the goal is one‐size‐fits all schooling. The programmed learning of today—moving through curricula paced to finish on time for testing and using filtered pedagogies designed to maximize standardized testing results—is just twentieth‐century efficiency and effectiveness, carrot and stick, management by ​objective, modernized through contemporary technologies and infused with algorithmic monitoring systems.

But in our work, we have learned that no average human exists, no median community does either. And we have learned that human learning is messy and complex, and that childhood, especially, is very messy, and very complex. Authentic opportunities for learners to create, design, build, engineer, and compose cannot truly coexist within the standardization model. That’s why tinkering around the edges, adding a “genius hour” to an otherwise unchanged school day, accomplishes nothing except to highlight all that’s wrong with our schools for this century.

A school cannot change without system change. Nothing can.

It is reckless to suppose that biodiversity can be diminished indefinitely without threatening humanity itself.

– E.O. Wilson Biodiversity Foundation (n.d.)

It doesn’t take long to figure out when observing the natural world that biodiversity creates pathways for organisms to not just survive, but also to thrive within ecosystems. Unlike the cornfields of Michigan where row after row of hybrid plants are identical to every other one, nature seems to appreciate differences among species. It’s a way of foolproofing longevity that stretches back generations across millennia, and the variety within and among species tends to support an entire ecosystem to sustain balance and thrive. In the scientific world, geneticists worry about our dependence upon crops that have been standardized genetically. The hybrid tomatoes keep longer in the grocery store, but the scientists know they are subject to potential blights that can wipe out the entire crop in a short period of time. It’s happened before – with corn, potatoes, and citrus crops. It’s why plant geneticists recommend never becoming reliant upon a single hybrid. It’s why ecologists know that biodiversity matters in an ecosystem. It’s the opposite of what we are doing inside the human ecology of our schools.​

We need variety and biodiversity in schools, too. The walls of schools are a contrived barrier that keeps kids and teachers apart within the system. The walls of schools keep new practices, tools, and strategies out and traditions in. When we think about creating a biodiversity of learning, we turn to new ways of thinking about how systems change. That doesn’t happen without removing barriers that wall off the potential for change. We have found that breaking walls is best interpreted through the ecological lens as defined by the work of Yong Zhao and Ken Frank, who framed the problem of introduction of a new species in Lake Michigan as having similarity to introducing a new practice, tool, or strategy into a school (ETEC 511 n.d.).

We also believe in the concept of terroir, used so beautifully as a metaphor by Margaret Wheatley and Deborah Frieze in Walk Out Walk On – that the soil and climate of two different continents produce variations in crops even when the seeds planted are the same (Wheatley and Frieze 2011). Schools are like that, too. Two schools may be situated in different terroir even though children work and play similarly no matter where we visit. However, those children grow up in different cultural contexts that shape what they bring with them into school. Educators do the same. Because of that, each school represents a unique identity, one shaped locally, not by the federal government. While school communities certainly benefit from cross‐pollinating of ideas and resources, allowing them to localize their identity makes a lot of sense when it comes to figuring out what children need to thrive as learners.

Together the concepts of biodiversity and terroir combine to support the idea that schools in different localities need the freedom to be different. It doesn’t mean that neurology research shouldn’t drive educators’ understanding of how children learn and the pedagogies they need to use in response to that understanding. It doesn’t mean a curricula free‐for‐all instead of a ​coherent focus developed locally. It doesn’t mean there shouldn’t be any sense of standards at all for what’s important to learn in and across disciplines. It does mean that broad parameters should allow children who need to learn about simple machines to do far more than simply memorize them for a test. It means that if a child or class is obsessed with simple machines, they don’t need to stop immediately to begin studying phases of the moon. When learning is allowed to be project, problem, and passion driven, then children learn because of their terroir, not disengage in spite of it. When we recognize biodiversity in our schools as healthy, then we increase the likelihood that our ecosystems will thrive.

Four Actions to Increase Learning Biodiversity in Your School Community

“We need more than a genius hour once a week to build learning agency” (Genius Hour n.d.). Analyze how covering content standards for a test at the expense of creating a deep context through exploration of integrated content and experience impacts students in your class, school, district. Write this down and share your perspectives with colleagues. What can you together do to begin to tackle the problem of coverage at the expense of learning?

Add a small makerspace in your room or school. It can be anywhere and it doesn’t need to have a lot of expensive technology to get it started. Our librarians say that glue sticks, cardboard, and duct tape are a great start to building a makerspace. Ask students “What do you want to make?” Watch them and see what happens.

When you use project‐oriented learning, break the parameter rules by reducing your own constraints on what students can do. Give choices. Get kids to ask questions about what they want to learn. Teach kids the McCrorie ISearch approach and let them construct projects in first person versus third person (Zorfass and Copel 1995). Accept different media submissions from videos to websites, not just a poster or a written report.

Unschool your projects. Abandon an “everyone does the same project” approach. Make more white spaces in your day to move beyond the standards. Begin by asking learners what they are interested in. Grab inspiration from their responses and find connections from their interests to questions they might pursue. Look for curricular intersections as you support them to collaborate with each other in pursuit of learning that’s intrinsically interesting to them. If you are tethered to standards, creates spaces every day for students to explore outside of that box using technology including ​devices, books, maker and art supplies, and experts in and out of class. Teach your children with their intrinsic drive in mind. Get them talking with each other. Record their questions. Make opportunities to share their work with their parents, the principal, and others in class. Invite parents into the community for learning exhibitions that represent biodiversity.

A much more accurate definition of an algorithm is that it’s an opinion embedded in math.

So, we do that every time we build algorithms — we curate our data, we define success, we embed our own values into algorithms.

So when people tell you algorithms make thing objective, you say “no, algorithms make things work for the builders of the algorithms.”

In general, we have a situation where algorithms are extremely powerful in our daily lives but there is a barrier between us and the people building them, and those people are typically coming from a kind of homogenous group of people who have their particular incentives — if it’s in a corporate setting, usually profit and not usually a question of fairness for the people who are subject to their algorithms.

So we always have to penetrate this fortress. We have to be able to question the algorithms themselves.

We live in the age of the algorithm – mathematical models are sorting our job applications, curating our online worlds, influencing our elections, and even deciding whether or not we should go to prison. But how much do we really know about them? Former Wall St quant, Cathy O’Neil, exposes the reality behind the AI, and explains how algorithms are just as prone to bias and discrimination as the humans who program them.

And then I made a big change. I quit my job and went to work as a quant for D. E. Shaw, a leading hedge fund. In leaving academia for finance, I carried mathematics from abstract theory into practice. The operations we performed on numbers translated into trillions of dollars sloshing from one account to another. At first I was excited and amazed by working in this new laboratory, the global economy. But in the autumn of 2008, after I’d been there for a bit more than a year, it came crashing down.

The crash made it all too clear that mathematics, once my refuge, was not only deeply entangled in the world’s problems but also fueling many of them. The housing crisis, the collapse of major financial institutions, the rise of unemployment- all had been aided and abetted by mathematicians wielding magic formulas. What’s more, thanks to the extraordinary powers that I loved so much, math was able to combine with technology to multiply the chaos and misfortune, adding efficiency and scale to systems that I now recognized as flawed.

If we had been clear-headed, we all would have taken a step back at this point to figure out how math had been misused and how we could prevent a similar catastrophe in the future. But instead, in the wake of the crisis, new mathematical techniques were hotter than ever, and expanding into still more domains. They churned 24/ 7 through petabytes of information, much of it scraped from social media or e-commerce websites. And increasingly they focused not on the movements of global financial markets but on human beings, on us. Mathematicians and statisticians were studying our desires, movements, and spending power. They were predicting our trustworthiness and calculating our potential as students, workers, lovers, criminals.

This was the Big Data economy, and it promised spectacular gains. A computer program could speed through thousands of résumés or loan applications in a second or two and sort them into neat lists, with the most promising candidates on top. This not only saved time but also was marketed as fair and objective.

Yet I saw trouble. The math-powered applications powering the data economy were based on choices made by fallible human beings. Some of these choices were no doubt made with the best intentions. Nevertheless, many of these models encoded human prejudice, misunderstanding, and bias into the software systems that increasingly managed our lives. Like gods, these mathematical models were opaque, their workings invisible to all but the highest priests in their domain: mathematicians and computer scientists. Their verdicts, even when wrong or harmful, were beyond dispute or appeal. And they tended to punish the poor and the oppressed in our society, while making the rich richer.

I came up with a name for these harmful kinds of models: Weapons of Math Destruction, or WMDs for short.

Equally important, statistical systems require feedback- something to tell them when they’re off track. Statisticians use errors to train their models and make them smarter. If Amazon. ​ com, through a faulty correlation, started recommending lawn care books to teenage girls, the clicks would plummet, and the algorithm would be tweaked until it got it right. Without feedback, however, a statistical engine can continue spinning out faulty and damaging analysis while never learning from its mistakes.

Many of the WMDs I’ll be discussing in this book, including the Washington school district’s value-added model, behave like that. They define their own reality and use it to justify their results. This type of model is self-perpetuating, highly destructive- and very common.

In WMDs, many poisonous assumptions are camouflaged by math and go largely untested and unquestioned.

This underscores another common feature of WMDs. They tend to punish the poor. This is, in part, because they are engineered to evaluate large numbers of people. They specialize in bulk, and they’re cheap. That’s part of their appeal. The wealthy, by contrast, often benefit from personal input. A white-shoe law firm or an exclusive prep school will lean far more on recommendations and face-to-face interviews than will a fast-food chain or a cash-strapped urban school district. The privileged, we’ll see time and again, are processed more by people, the masses by machines.

Needless to say, racists don’t spend a lot of time hunting down reliable data to train their twisted models. And once their model morphs into a belief, it becomes hardwired. It generates poisonous assumptions, yet rarely tests them, settling instead for data that seems to confirm and fortify them. Consequently, racism is the most slovenly of predictive models. It is powered by haphazard data gathering and spurious correlations, reinforced by institutional inequities, and polluted by confirmation bias. In this way, oddly enough, racism operates like many of the WMDs I’ll be describing in this book.

Indeed. These three great books provide a systems view of higher education and its intersections with tech and algorithms. Below, I excerpt from their introductions and book blurbs, provide chapter lists, and select a handful of tweets from authors Tressie McMillan Cottom, Sara Goldrick-Rab, and Cathy O’Neil. They are all active on Twitter and well worth a follow.

This book is about the power of algorithms in the age of neoliberalism and the ways those digital decisions reinforce oppressive social relationships and enact new modes of racial profiling, which I have termed technological redlining. By making visible the ways that capital, race, and gender are factors in creating unequal conditions, I am bringing light to various forms of technological redlining that are on the rise. The near-ubiquitous use of algorithmically driven software, both visible and invisible to everyday people, demands a closer inspection of what values are prioritized in such automated decision-making systems. Typically, the practice of redlining has been most often used in real estate and banking circles, creating and deepening inequalities by race, such that, for example, people of color are more likely to pay higher interest rates or premiums just because they are Black or Latino, especially if they live in low-income neighborhoods. On the Internet and in our everyday uses of technology, discrimination is also embedded in computer code and, increasingly, in artificial intelligence technologies that we are reliant on, by choice or not. I believe that artificial intelligence will become a major human rights issue in the twenty-first century. We are only beginning to understand the long-term consequences of these decision-making tools in both masking and deepening social inequality. This book is just the start of trying to make these consequences visible. There will be many more, by myself and others, who will try to make sense of the consequences of automated decision making through algorithms in society.

Part of the challenge of understanding algorithmic oppression is to understand that mathematical formulations to drive automated decisions are made by human beings. While we often think of terms such as “big data” and “algorithms” as being benign, neutral, or objective, they are anything but. The people who make these decisions hold all types of values, many of which openly promote racism, sexism, and false notions of meritocracy, which is well documented in studies of Silicon Valley and other tech corridors.

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We have basically told these companies that the smart thing to do, the shareholder thing to do, is to lie and to break the law. Now technology is 99% about shareholder value and 1% about the betterment of humanity. The markets are failing. Source: Scott Galloway Says Amazon, Apple, Facebook, And Google should be broken […]

…find a love for identity politics…so that we can draw battle lines between those who want shame to grow on trees and those who want to overcome it. Source: Video Episode 310: Live from the New York Comedy Festival 2018 @58:30 | Harmontown Insightful monologue on shame and vulnerability starting @51:00. Shame is not a […]

Along with phrases appropriated directly from the so-called alt-right, a small group of neotraditionalist educators have invented the concept of ‘school shaming’ to make their reactionary politics seem, well, less reactionary. Criticize a school for how it treats students, and you’re ‘school shaming’. Talk about structural racism and curriculum, and you’re playing ‘identity politics’. Oppose […]

“Any authority within the space must be aimed at fostering agency in all the members of the community. And this depends on a recognition of the power dynamics and hierarchies that this kind of learning environment must actively and continuously work against. There is no place for shame in the work of education.” Source: Dear […]

Most cyborgs are disabled people who interface with technology. We depend on a computer for some major bodily function. The tryborg – a word I invented – is a nondisabled person who has no fundamental interface. The tryborg is a counterfeit cyborg. The tryborg tries to integrate with technology through the latest product or innovation. […]

A great example of how to check that you are accommodating diverse learners was shared in the Panel at the end of the conference: Walk through your learning environment as different personas (think different ethnicities, students in wheelchairs, someone with ASD etc.) and see how inclusive it is. Do the spaces allow for you to […]

The irony of turning schools into therapeutic institutions when they generate so much stress and anxiety seems lost on policy-makers who express concern about children’s mental health. Source: ClassDojo app takes mindfulness to scale in public education | code acts in education